https://github.com/alan-turing-institute/spatial-inequality
https://github.com/alan-turing-institute/spatial-inequality
hacktoberfest hut23 hut23-244
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/alan-turing-institute/spatial-inequality
- Owner: alan-turing-institute
- License: mit
- Created: 2019-12-05T11:06:16.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-08-06T23:31:20.000Z (9 months ago)
- Last Synced: 2024-08-07T02:38:35.051Z (9 months ago)
- Topics: hacktoberfest, hut23, hut23-244
- Language: Jupyter Notebook
- Homepage:
- Size: 273 MB
- Stars: 9
- Watchers: 5
- Forks: 2
- Open Issues: 32
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# spatial-inequality
Repository for the [Spatial Inequality in the Smart City](https://www.turing.ac.uk/research/research-projects/spatial-inequality-and-smart-city) project.
## Papers and Publications
If you're looking for the scripts used to generate the figures in the paper "Optimising for equity: Sensor coverage, networks and the responsive city", please go to the [OptimisingForEquity](publications/OptimisingForEquity) directory.
## Pre-requisites
1. [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
2. [Conda](https://docs.conda.io/en/latest/miniconda.html)
## Setup
1. Clone the repo:
```shell
git clone https://github.com/alan-turing-institute/spatial-inequality.git
```
2. Change to the `spatial-inequality` directory:
```shell
cd spatial-inequality
```
3. Create the conda environment:
```
conda env create
```
4. Activate the environment:
```
conda activate spatial-inequality
```
## API
### Start the API
```bash
docker-compose up
```
Should then be available on `0.0.0.0:5000`
Command to force a rebuild if something hasn't udpated correctly:
```bash
docker-compose up --build --force-recreate
```
### Submitting Optimisation Jobs with SocketIO
Generate a pseudo-optimised network of sensors with a greedy algorithm. See `scripts/client.py` for an example.
* Submit an optimisation job:
- Client emits event `submitJob` with data `{"n_sensors": 10, "theta": 500}`
- Server emits event `job` with job data.
- On job completion, server emits `jobFinished` with job_id and result.
- Optionally can give the following parameters with `submitJob`:
- `min_age`: Minimum age to include for population coverage (default: 0)
- `max_age`: Maximum age to include for population coverage (default: 90)
- `population_weight`: Weight for population coverage (default: 1)
- `workplace_weight`: Weight for place of work coverage (default: 0)
* Retrieve the result/status of a job:
- Client emits event `getJob` with data ``
- Server emits event `job` with job data.
* Get a list of available job IDs:
- Client emits event `getQueue`
- Server emits event `queue` with queue data.
* Remove a job from the queue:
- Client emits event `deleteJob` with data ``
- Server emits event `message` with deletion result.
* Remove all jobs from the queue:
- Client emits event `deleteQueue`
- Server emits event `message` with deletion result.
### Coverage Endpoint
The `/coverage` endpoint computes coverage for a user-defined network (set of sensors placed at output area centroids), and takes a JSON with the following format:
```json
{
"sensors": ["E00042671","E00042803"],
"theta": 500,
"lad20cd": "E08000021"
}
```
where `theta` and `lad20cd` are optional and take the values above by default, but sensors must be defined and takes a list of `oa11cd` codes that have sensors in them.
It should return a JSON with this structure:
```json
{
"oa_coverage":[
{"coverage":0.6431659719289781,"oa11cd":"E00042665"},
...,
],
"total_coverage": {
"pop_children":0.0396946631327479,
"pop_elderly":0.024591629984248815,
"pop_total":0.059299090984356984,
"workplace":0.0947448314996531
}
}
```
### Dependencies
The dockerised version of the code used for the API uses only `pip` and the packages in `requirements.txt`. This doesn't include some plotting and optimisation libraries included in the `conda` environment.